A cornerstone of radiological diagnostics is a comparison of current examination with prior ones. The usage of complementary examinations e.g. PET-CT for metabolic and anatomic information, MRI (good contrast resolution) and CT (good spatial resolution and detection of calcifications) is also widely spread.
A partial list of procedures where two or more data sets are used for diagnosis:                1. Tumor management (inter and intra modality)                    a. Tumor (type) analysis (are there calcification, hemorrhage, etc.)            b. Treatment efficiency and growth assessment                        2. Lymph nodes follow-up (PET-CT)        3. Trauma follow-up (e.g. clearance of intracranial hemorrhage).        4. Brain stroke management (CT-MRI)        5. Spine disk disease MRI and CT and follow-up        6. Knee imaging—MRI and CT        7. Pre and post operation follow-up e.g. evacuation of inflammatory collections.        
For efficient usage of multiple data sets of the same pathology in current common visualization tools the data sets must agree almost exactly in spatial scan parameters e.g. orientation of plane of cross section, spacing between slices, thickness and in-plane resolution. However, examinations taken at different time, locations and source modality tend to differ in spatial parameters.
Typical reading workflow includes examining the current data set, comparing the assessment results to the prior and complementary data sets, taking measurements of pertinent features and reporting the findings and results. In majority of examinations the tools that are used are merely adjustments of imaging parameters (e.g. zoom and window level) hence most of the time is spent on actual diagnosis and not on manipulation of the data. State of the art reading applications supply sophisticated tools for manipulating a single data set. The work load of manually adjusting the data sets puts a significant damper on the reading process and may prevent meticulous comparison or efficient use of complementary data sets. A procedure that can facilitate a reliable diagnosis when the orientation or pixel spacing of two data sets is dissimilar includes volumetric registration (matching) of the data sets. Presently, this procedure is limited in use to dedicated post-processing workstations only, where an intervention of the operator is usually required, resulting in a lengthy operation and low reproducibility since it relies on a manual operation. Thus, it is not considered suitable for the demands of a routine high-throughput radiology diagnostics.